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Choking and Excelling Under Pressure in Classification

Choking and Excelling Under Pressure in Classification

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Choking and Excelling Under Pressure in Classification

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  1. Choking and Excelling Under Pressure in Classification W. Todd Maddox & Arthur B. Markman University of Texas, Austin Major Collaborators: Grant Baldwin Brian GlassLisa GrimmDarrell Worthy This work is supported by AFOSR grant FA9550-06-1-0204 and NIMH grant R01MH077708

  2. Overview of Talk • Why care about motivation? • Pressure (stress) induces a motivational state that affects cognition • A framework for thinking about motivation and its influence on cognition and learning • Pressure (stress) effects • Application to: • Perceptual classification learning • Pressure effects on classification • learning and experienced behavior

  3. Motivation and Cognition • Why care about motivation? • No behavior without motivation • Motivation affects cognition • Approach positive states and avoid negative states • Motivation rarely controlled in cognitive experiments • No principled distinctions between motivational and cognitive brain regions • Motivation and Cognition need to be studied together

  4. Motivation and Pressure (Stress) • Two current theories of pressure effects on cognition • Distraction: WM resources reduced • Monitoring: Increased monitoring of explicit processes • We suggest that pressure affects cognition through its effects on the motivational state • Working Hypothesis: • Pressure induces an “avoidance” or “prevention” motivational state

  5. Regulatory Fit Framework • Global Motivational Orientation (Higgins: Regulatory Focus Theory) • Approach: Sensitivity to gains in environment (Promotion focus) • Avoidance: Sensitivity to losses in environment (Prevention focus) • Pressure associated with Avoidance (prevention) state • Local Task Reward Structure • Gains in the environment • Losses in the environment • Regulatory focus interacts with task reward structure

  6. Consider the bigger picture • Almost all cognitive research involves a promotion focus and a gains reward structure • Recall Pressure associated with prevention (mismatch) Fit

  7. Regulatory Fit • Regulatory focus interacts with task reward structure • Regulatory fit increases “flexible” cognitive processing. • Flexibility can be defined within tasks • Willingness to test various strategies • Willingness to explore the environment • Hold off question of “why” for now

  8. Perceptual Classification Maddox, Baldwin & Markman (2006; Memory & Cognition)

  9. Perceptual Classification Task • Stimuli with small number of underlying dimensions • Lines that vary in length, orientation and position • Experimenter control of category structure • Extensive set of tools for modeling performance of individual participants • Can assess the strategies participants use in the task

  10. Scatterplot of Stimuli o = category A = long, steep lines + = category B = all others

  11. Possible Rule-based Strategies 83% accuracy 100% accuracy

  12. Regulatory Focus(Global Task Goal) 90% correct yields “bonus”

  13. Task Reward Structure(Local Trial-by-trial Task Goal)

  14. Experiment Screen Sample Gains Losses

  15. Yes Bonus No 18 0

  16. Yes Bonus No 21 18 Correct 0

  17. Yes Bonus No 21 0

  18. Yes Bonus No 22 Wrong, that was an A 0

  19. Experiment Set 1Flexibility is Advantageous • Conjunctive Rule-Based Task • Exploration of verbal rule space required

  20. Performance Results Plots averaged over blocks. Effects generally larger early in learning

  21. Conclusions • In a classification task where exploration of the verbal rule space is advantageous, a regulatory fit led to better performance.

  22. Experiment Set 2Flexibility is Disadvantageous • Information-Integration Task

  23. Experimental Method identical to Experiment Set 1

  24. Prediction • If regulatory fit = more flexibility, then rule-based strategies should persist leading to poorer performance • COVIS assumes that rule-based strategies dominate early. • Rule-based strategies must be abandoned in information-integration tasks • Exceeding the bonus requires abandoning rule-based strategies

  25. Performance Results (Losses)

  26. Conclusions • We observe a 3-way interaction between regulatory focus, task reward structure, and nature of the task. • Flexibility advantageous: Fit is good • Flexibility disadvantageous: Fit is bad

  27. Application 2:Choking Under Pressure Markman, Maddox & Worthy (2006; Psychological Science)

  28. Choking Under Pressure • Anecdotal phenomenon (e.g. sports, test-taking, etc.) • People perform worse than normal when under pressure • Might pressure be similar to a prevention focus?

  29. Categorization Tasks Rule-Based Information-Integration

  30. Method • Gains only • Low pressure – “do your best” • High pressure: -Paired with a ‘partner’ -If both of you reach criterion, both get $6 -If one of you fails neither get $6 bonus -Partner reached criterion

  31. Predictions

  32. Results

  33. Summary • Pressure does appear to operate like a prevention focus during classification learning (at least with gains). • Pressure (a mismatch with gains) hurts rule-based learning, but helps information-integration learning.

  34. Pressure and Experience • Method • 2500 trials over 4 sessions with NO pressure manipulation • 5th session: pressure or no pressure • 5th session final block: super pressure • No error in 80 trials yields $100 (no partner)

  35. Categorization Tasks Rule-Based Information-Integration

  36. Final Control vs. First Pressure Block • Final control accuracy high (90% in all conditions • Pressure improves II, but not RB (interaction nearly significant)

  37. Final Pressure (or Control) vs. Super Pressure Block • Super pressure adversely affected both RB conditions • Super pressure adversely affected the II Control condition • Super pressure accentuated the II Pressure condition

  38. Summary • Pressure generally improves II performance even when experienced. • Pressure generally hurts RB performance even when experienced.

  39. Related Findings • Interaction between Regulatory Focus, Reward Structure and Task • Binary category structures: Same pattern • Card selection task: • More exploratory behavior for Regulatory Fit • May explain some stereotype threat • A stereotype threat induces prevention focus • Participants perform better under losses than gains

  40. Future Research • Cognitive mechanisms in this effect • Role of working memory? • Relationship to individual differences • Personality variables • Cultural difference variables • Other tasks • Foraging • Wisconsin Card Sorting Task

  41. Thank You

  42. Super Pressure Block First Trial Missed • Final control accuracy high (???%) • Equivalent across RB and II conditions • Pressure improves II, but not RB

  43. Why we should care • Motivation affects decision making • Preferences (Brendl, Markman, & Messner; Ferguson & Bargh) • Need to smoke increases preference for smoking related items and reduces preference for not smoking related items • Goal-adoption (Aarts et al, Fishbach & Shah) • People adopt goals of people around them. • Selection of optimal behavior (Bechara et al., Busemeyer & Townsend) • All cognitive research has an (uncontrolled) motivational component • “motivate” to “try harder” • “Motivation” brain regions reciprocally connected with “cognitive” brain regions

  44. Regulatory Fit Framework • Hypothesis: A “fit” between global task goals (regulatory focus) and local task goals (task reward structure) increases “flexible” cognitive processing. • Flexibility can be defined within tasks • Willingness to test various strategies • Willingness to explore the environment • Hold off question of “why” for now?

  45. Regulatory Fit = Flexibility: Why? • Empirical support in several domains • Connection to Neuroscience • Positive affect-frontal dopamine-flexibility hypothesis (Isen, Ashby, etc) • Regulatory focus-frontal activation findings (Amodio, Cunningham, etc) • LC-NE-exploration/exploitation relation (Ashton-Jones, Cohen, Daw)

  46. Rule-Based Modeling • More low pressure (fit) subjects were best fit by the rule-based models. • More high pressure (mismatch) subjects are random.

  47. Information integration modeling • More high pressure subjects best fit by an information integration model. • More low pressure subjects random.

  48. Studying Regulatory Fit Effects • How can we study this systematically? • Need task for which we can manipulate the advantageousness of flexibility, while holding other task characteristics fixed • Need a good manipulation of regulatory focus • Need to be able to manipulate reward structure

  49. Choking Under Pressure • Basketball data • Free throw during last minute of game

  50. Model-based Analyses • Decision-bound models (Ashby & Maddox) fit to each participant block-by-block